Loss-on-ignition estimation apparatus, loss-on-ignition estimation method, machine-learning apparatus, and machine-learning method

ABSTRACT

An object is to accurately estimate loss-on-ignition in a short time. A loss-on-ignition estimation apparatus includes at least one processor configured to carry out an estimation step, the estimation step including estimating the loss-on-ignition of foundry sand with use of a learned model constructed by means of machine learning. The learned model is configured to receive, as input, (1) sand weight data relating to a weight of the foundry sand detected in a calcination period and (2) at least one of (i) sand property data relating to one or more properties of the foundry sand, (ii) additive data relating to one or more additives added to the foundry sand, and (iii) calcination environment data relating to a calcination environment detected in the calcination period. The learned model is configured to generate, as output, an estimated loss-on-ignition of the foundry sand.

This Nonprovisional application claims priority under 35 U.S.C. § 119 onPatent Application No. 2020-063305 filed in Japan on Mar. 31, 2020, theentire contents of which are hereby incorporated by reference.

FIELD OF INVENTION

The present invention relates to an apparatus and method for estimatingthe loss-on-ignition of foundry sand using a learned model. The presentinvention also relates to an apparatus and method for constructing sucha learned model.

BACKGROUND OF INVENTION

Foundry sand that hardens via a chemical reaction of a hardening agent(sand for use in, for example, a self-hardening process or gas-hardeningprocess) is widely used as one of the various types of foundry sand foruse in making a mold. Foundry sand used in casting is polished in a sandreclaimer, mixed with a resin and a hardening agent in a mixing machine,and then reused in molding. In so doing, the conditions under which thesand reclaimer, the mixing machine, and the like operate are optimizedin accordance with the loss-on-ignition of the foundry sand used incasting.

The loss-on-ignition of foundry sand means the amount of resin remainingin the foundry sand used in casting, and is determined by drying andthen calcining the foundry sand. The temperature at which the foundrysand is calcined is 1000° C., and the time for which the foundry sand iscalcined is 60 minutes. A widely used definition for loss-on-ignition is(W0−W60)/W0, where W0 [g] is the weight of dried but not calcinedfoundry sand and W60 is the weight of calcined foundry sand. Theloss-on-ignition is also called “LOI”.

However, for the loss-on-ignition to be determined using theabove-stated definition, drying needs to be carried out for 60 minutesand calcination needs to be carried out for 60 minutes; that is, ittakes 120 minutes or longer in total to determine the loss-on-ignition.Therefore, the determination of loss-on-ignition is a significant timeburden for foundrymen. Furthermore, the operating conditions of the sandreclaimer and the mixing machine cannot be optimized according to theloss-on-ignition over a long time while carrying out measurement todetermine the loss-on-ignition. This causes a decrease in quality ofcastings.

CITATION LIST

-   -   Japanese Patent Application Publication, Tokukai, No.        2015-208781

SUMMARY OF INVENTION

The present invention is to provide a technique that is capable ofaccurately estimating loss-on-ignition in a short time.

An apparatus configured to estimate loss-on-ignition in accordance withan aspect of the present invention includes at least one processorconfigured to carry out an estimation step. The estimation step includesestimating a loss-on-ignition of foundry sand with use of a learnedmodel constructed by means of machine learning. A method ofloss-on-ignition estimation in accordance with an aspect of the presentinvention includes an estimation step in which at least one processorestimates a loss-on-ignition of foundry sand with use of a learned modelconstructed by means of machine learning.

A machine-learning apparatus in accordance with an aspect of the presentinvention includes at least one processor configured to carry out aconstruction step. The construction step includes constructing, by meansof supervised learning using a dataset-for-learning, a learned modelconfigured to estimate a loss-on-ignition. A machine-learning method inaccordance with an aspect of the present invention includes aconstruction step in which at least one processor constructs, by meansof supervised learning using a dataset-for-learning, a learned modelconfigured to estimate a loss-on-ignition.

The apparatus configured to estimate loss-on-ignition, the method ofloss-on-ignition estimation, the machine-learning apparatus, and themachine-learning method are each configured such that: (a) the learnedmodel is configured to receive, as input, (1) sand weight data relatingto a weight of the foundry sand detected in a calcination period and (2)at least one of (i) sand property data relating to one or moreproperties of the foundry sand, (ii) additive data relating to one ormore additives added to the foundry sand, and (iii) calcinationenvironment data relating to a calcination environment detected in thecalcination period; and (b) the learned model is configured to generate,as output, an estimated loss-on-ignition of the foundry sand or anestimated weight of the foundry sand after a predetermined period ofcalcination, the predetermined period being longer than the calcinationperiod.

An apparatus and method for loss-on-ignition estimation in accordancewith an aspect of the present invention make it possible to accuratelyestimate loss-on-ignition in a short time. A machine-learning apparatusand a machine-learning method in accordance with an aspect of thepresent invention make it possible to construct a learned model for usein such an apparatus and method for loss-on-ignition estimation.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a configuration of a loss-on-ignition estimationsystem in accordance with an aspect of the present invention.

FIG. 2 is a block diagram illustrating a configuration of aloss-on-ignition estimation apparatus included in the loss-on-ignitionestimation system of FIG. 1.

FIG. 3 is a flowchart illustrating a loss-on-ignition estimation methodcarried out by the loss-on-ignition estimation apparatus of FIG. 2.

FIG. 4 is a block diagram illustrating a configuration of amachine-learning apparatus included in the loss-on-ignition estimationsystem of FIG. 1.

FIG. 5 is a flowchart illustrating a machine-learning method carried outby the machine-learning apparatus of FIG. 4.

FIG. 6 is a flowchart illustrating a sand conditioning cycle to whichthe loss-on-ignition estimation system of FIG. 1 is applied.

FIG. 7 illustrates a specific example of a sand reclaimer which carriesout a sand reclamation step included in the sand conditioning cycle ofFIG. 6.

FIG. 8 illustrates a specific example of a mixing machine which carriesout a mixing step included in the sand conditioning cycle of FIG. 6.

DESCRIPTION OF EMBODIMENTS

[Loss-On-Ignition Estimation System]

The following description will discuss a loss-on-ignition estimationsystem S in accordance with an embodiment of the present invention withreference to FIG. 1. FIG. 1 illustrates a configuration of theloss-on-ignition estimation system S.

The loss-on-ignition estimation system S is configured to estimate theloss-on-ignition of recovered sand or reclaimed sand (recovered sand andreclaimed sand are hereinafter each referred to as “foundry sand”,unless a particular distinction is required) generated in a castingcycle C (described later with reference to FIG. 6) that includes acasting phase C1 and a sand reclamation phase C2. As illustrated in FIG.1, the loss-on-ignition estimation system S includes a loss-on-ignitionestimation apparatus 1 (apparatus configured to estimateloss-on-ignition), a machine-learning apparatus 2, a calcining apparatus3, a sensor group 4, and a data logger 5.

The calcining apparatus 3 is configured to calcine a small amount (forexample, not less than 2 g and not more than 10 g) of foundry sand takenas a sample. As illustrated in FIG. 1, the calcining apparatus 3includes a crucible 31 and a furnace 35.

The crucible 31 is configured to hold foundry sand. The furnace 35 isconfigured to heat the foundry sand held in the crucible 31. The furnace35 controls the temperature inside the furnace 35 such that a dryingstep is carried out over a predetermined period of time (hereinafter“drying time”, e.g., 60 minutes) and then a calcination step is carriedout over a predetermined period of time (hereinafter “calcination time”,e.g., 10 minutes). It is noted here that the drying step involvesevaporating water contained in the foundry sand at a predeterminedtemperature (hereinafter “drying temperature”, e.g., 100° C.). Also notethat the calcination step involves burning combustible residues(residues resulted from a resin and a hardening agent added to thefoundry sand) attached to grains of the foundry sand at a predeterminedtemperature (hereinafter “calcination temperature”, e.g., 1000° C.). Thecalcination time is shorter than 60 minutes (calcination time employedin a conventional method of measuring loss-on-ignition). There isprovided an elevating mechanism configured to raise and lower thecrucible 31 (or the furnace 35). When introducing foundry sand orrecovering foundry sand, the elevating mechanism lowers the crucible 31(or raises the furnace 35) to cause the crucible 31 to be locatedoutside the furnace 35. On the contrary, when heating foundry sand, theelevating mechanism raises the crucible 31 (or lowers the furnace 35) tocause the crucible 31 to be located inside the furnace 35.

The sensor group 4 includes an electronic balance 41, thermometers 42 to44, a pressure gauge 45, and a flowmeter 46.

The electronic balance 41 is a sensor to measure the weight of foundrysand (such a weight is hereinafter referred to as “sand weight”) held inthe crucible 31. The thermometer 42 is a sensor to measure thetemperature of a gas inside the furnace 35 (such a temperature ishereinafter referred to as “furnace's internal temperature”). Thethermometer 43 is a sensor to measure the temperature of the outer wallof the furnace 35 (such a temperature is hereinafter referred to as“furnace temperature”). The thermometer 44 is a sensor to measure thetemperature of the environment in which the calcining apparatus 3 ispresent (such a temperature is hereinafter referred to as “ambienttemperature”). The pressure gauge 45 is a sensor to measure the pressureof a gas inside the furnace 35 (such a pressure may be hereinafterreferred to as “gas pressure”). The flowmeter 46 is a sensor to measurethe flow rate of a gas discharged from the furnace 35 (such a flow rateis hereinafter referred to as “gas quantity”). Note that the gas insidethe furnace 35 and the gas discharged from the furnace 35 are composedmainly of a gas resulting from vaporization of combustible residues.

The data logger 5 is a device configured to acquire output signals fromthe sensors of the sensor group 4 and provide the loss-on-ignitionestimation apparatus 1 and the machine-learning apparatus 2 with datarepresenting sand weight, furnace's internal temperature, furnacetemperature, ambient temperature, gas pressure, and gas quantity. Thedata logger 5 can be composed of, for example, a programmable logiccontroller (PLC), an industrial PC (IPC), or the like.

The loss-on-ignition estimation apparatus 1 is configured to carry out aloss-on-ignition estimation method (method of loss-on-ignitionestimation) M1. The loss-on-ignition estimation method M1 involvesestimating loss-on-ignition from data provided from the data logger 5and data inputted by an operator with use of a learned model LMconstructed by means of machine learning. Examples of the learned modelLM include algorithms such as neural network models (e.g., convolutionalneural network and recurrent neural network), regression models (e.g.,linear regression), and tree models (e.g., regression tree). Aconfiguration of the loss-on-ignition estimation apparatus 1 and a flowof the loss-on-ignition estimation method M1 will be described later indetail with reference to different drawings.

The learned model LM is configured to receive, as input: sand weightdata relating to the weight of foundry sand detected in a predeterminedperiod from a first point in time at which the furnace's internaltemperature reached a predetermined calcination temperature to a secondpoint in time which is a predetermined calcination time after the firstpoint in time (such a period may be hereinafter referred to as“calcination period”); sand property data relating to one or moreproperties of the foundry sand; additive data relating to one or moreadditives added to the foundry sand; and calcination environment datarelating to a calcination environment detected in the calcinationperiod. Note that examples of the additives include resins and hardeningagents.

The sand weight data contains sand weight acquired from the electronicbalance 41 via the data logger 5. The sand weight contained in the sandweight data may be an entire time series of sand weight detected in thecalcination period or may be a value of sand weight detected at aspecific point in time in the calcination period or values of sandweight detected at specific points in time included in the calcinationperiod. In the present embodiment, sand weight detected at the point intime at which the calcination period started (such sand weight may behereinafter referred to as “sand weight at the start of calcination”)and sand weight detected at the point in time at which the calcinationperiod ended (such sand weight may be hereinafter referred to as “sandweight at the end of calcination) are inputted to the learned model LM.Note that the sand weight at the start of calcination may be measured inthe following manner: the crucible 31 is taken out of the furnace 35after the drying step but before the calcination step. Also note thatthe sand weight at the end of calcination may be measured in thefollowing manner: the crucible 31 is taken out of the furnace 35 afterthe calcination step.

The sand property data contains sand type, sand-to-metal ratio, amountof new sand introduced, and capacitance which have been inputted by anoperator. As used herein, the term “sand type” refers to the type offoundry sand used in the casting cycle C. The sand type can be, forexample, artificial sand, silica sand, chromite sand, zircon sand, acombination of any of such sand types, or the like. Note that thefollowing configuration may be employed: in a case where the foundrysand used in the casting cycle C is a mixture of a plurality of sandtypes, an operator inputs the composition of the foundry sand to theloss-on-ignition estimation apparatus 1. The term “sand-to-metal ratio”refers to the ratio of the weight of a mold made in the casting cycle Cto the weight of a casting which is made in the casting cycle C. Theterm “amount of new sand introduced” refers to the amount (which may beweight or volume) of new sand introduced into reclaimed sand in thecasting cycle C per unit time (in a case of continuous process) or perbatch (in a case of batch process). The term “capacitance” refers to thecapacitance of foundry sand taken as a sample. Note that the capacitanceof foundry sand can be measured by, for example, the method disclosed inPatent Literature 1.

The additive data contains resin type, amount of resin added, hardeningagent type, and amount of hardening agent added which have been inputtedby an operator. As used herein, the term “resin type” refers to the typeof resin(s) added to foundry sand in the casting cycle C. The resin typecan be, for example, furan, alkaline phenol, phenolic urethane, waterglass, and/or the like. The term “amount of resin added” refers to theamount (which may be weight or volume) of resin(s) added to foundry sandin the casting cycle C per unit time (in a case of continuous process)or per batch (in a case of batch process). The term “hardening agenttype” refers to the type of hardening agent(s) added, together withresin(s), to the foundry sand in the casting cycle C. The hardeningagent type can be, for example, organic sulfonic acid, organic ester,and/or the like. The term “amount of hardening agent added” refers tothe amount (which may be weight or volume) of hardening agent(s) added,together with resin(s), to the foundry sand in the casting cycle C perunit time (in a case of continuous process) or per batch (in a case ofbatch process).

The calcination environment data contains furnace's internaltemperature, furnace temperature, ambient temperature, gas pressure, andgas quantity which have been acquired from the sensor group 4 via thedata logger 5. Note, however, that the furnace's internal temperatureinputted to the learned model LM may be an entire time series offurnace's internal temperature detected in the calcination period, theaverage of values of furnace's internal temperature detected in thecalcination period, or a value of furnace's internal temperaturedetected at a specific point in time included in the calcination periodor values of furnace's internal temperature detected at specific pointsin time included in the calcination period. In the present embodiment,the average of values of furnace's internal temperature (hereinafter maybe referred to as “average furnace's internal temperature”) is inputtedto the learned model LM. The furnace temperature inputted to the learnedmodel LM may be an entire time series of furnace temperature detected inthe calcination period, the average of values of furnace temperaturedetected in the calcination period, or a value of furnace temperaturedetected at a specific point in time included in the calcination periodor values of furnace temperature detected at specific points in timeincluded in the calcination period. In the present embodiment, theaverage of values of furnace temperature (hereinafter may be referred toas “average furnace temperature”) is inputted to the learned model LM.The ambient temperature inputted to the learned model LM may be anentire time series of ambient temperature detected in the calcinationperiod, the average of values of ambient temperature detected in thecalcination period, or a value of ambient temperature detected at aspecific point in time included in the calcination period or values ofambient temperature detected at specific points in time included in thecalcination period. In the present embodiment, the average of values ofambient temperature (hereinafter may be referred to as “average ambienttemperature”) is inputted to the learned model LM. The gas pressureinputted to the learned model LM may be an entire time series of gaspressure detected in the calcination period, the average of values ofgas pressure detected in the calcination period, or a value of gaspressure detected at a specific point in time included in thecalcination period or values of gas pressure detected at specific pointsin time included in the calcination period. In the present embodiment,the average of values of gas pressure (hereinafter may be referred to as“average gas pressure”) is inputted to the learned model LM. The gasquantity inputted to the learned model LM may be an entire time seriesof gas quantity detected in the calcination period, the average ofvalues of gas quantity detected in the calcination period, or a value ofgas quantity detected at a specific point in time included in thecalcination period or values of gas quantity detected at specific pointsin time included in the calcination period. In the present embodiment,the average of values of gas quantity (hereinafter may be referred to as“average gas quantity”) is inputted to the learned model LM. Thecalcination environment data further contains the rate of temperaturerise. The rate of temperature rise is calculated by the loss-on-ignitionestimation apparatus 1 based on the furnace's internal temperatureacquired from the sensor group 4 via the data logger 5. The rate oftemperature rise is calculated using the following equation, forexample: rate of temperature rise=(furnace's internal temperature attime at which calcination step started −furnace's internal temperatureat time at which furnace started to operate)/(time at which calcinationstep started−time at which furnace started to operate).

The learned model LM is configured to generate loss-on-ignition asoutput. Loss-on-ignition is defined by (W0−W60)/W0, where W0 is theweight of dried but not calcined foundry sand, W60 is the weight ofcalcined foundry sand, and the calcination of the foundry sand here iscarried out at a temperature of 1000° C. for 60 minutes. Determinationof loss-on-ignition using this definition necessitates calcining foundrysand for 60 minutes or more; however, the use of the learned model LMmakes it possible to estimate loss-on-ignition from sand weight dataobtained by carrying out calcination for, for example, about 10 minutes,which is shorter than 60 minutes. Furthermore, there is a constantcorrelation between (i) the sand property data, additive data, andcalcination environment data and (ii) loss-on-ignition. Therefore, withthe use of the learned model LM configured to receive not only the sandweight data but also the sand property data, the additive data, and thecalcination environment data as input, it is possible to accuratelyestimate loss-on-ignition in a short time.

The machine-learning apparatus 2 is configured to carry out amachine-learning method M2. The machine-learning method M2 involves:constructing a dataset-for-learning DS with use of data provided fromthe data logger 5 and data inputted by an operator; and constructing alearned model LM by means of machine learning (supervised learning)using the dataset-for-learning DS. A configuration of themachine-learning apparatus 2 and a flow of the machine-learning methodM2 will be described later in detail with reference to differentdrawings.

The loss-on-ignition estimation system S proceeds through a preparatoryphase and a trial phase and enters an actual use phase. The followingdescription will roughly discuss what the preparatory phase, the trialphase, and the actual use phase are.

(1) Preparatory Phase

In the preparatory phase, an operator carries out 60-minute calcinationto determine loss-on-ignition. Every time the operator carries out thedetermination of loss-on-ignition, the machine-learning apparatus 2prepares training data from data provided from the data logger 5 anddata inputted by the operator (including the loss-on-ignition calculatedusing the definition), and add the prepared training data to thedataset-for-learning DS. The preparatory phase may end upon passage of acertain period (for example, one week, one month, one year, or the like)from the start of the preparatory phase or may end when the number oftimes the determination of loss-on-ignition has been carried out in thepreparatory phase has reached a certain number (for example, 100, 1000,10000 or the like). Upon completion of the preparatory phase, themachine-learning apparatus 2 constructs a learned model LM by means ofmachine learning using the dataset-for-learning DS. The constructedlearned model LM is transferred from the machine-learning apparatus 2 tothe loss-on-ignition estimation apparatus 1.

(2) Trial Phase

In the trial phase, an operator carries out 60-minute calcination todetermine loss-on-ignition, and the loss-on-ignition estimationapparatus 1 estimates loss-on-ignition. Every time the operator carriesout the determination of loss-on-ignition, the loss-on-ignitionestimation apparatus 1 estimates loss-on-ignition with use of thelearned model LM based on data provided from the data logger 5 and datainputted by an operator. The trial phase may end upon passage of acertain period (for example, one week, one month, one year, or the like)from the start of the trial phase or may end when the number of timesthe determination of loss-on-ignition has been carried out in the trialphase has reached a certain number (for example, 100, 1000, 10000 or thelike). Upon completion of the trial phase, the operator compares thevalues of the loss-on-ignition calculated using the definition and thevalues of the loss-on-ignition estimated by the loss-on-ignitionestimation apparatus 1, and evaluates the accuracy of the estimationmade by the loss-on-ignition estimation apparatus 1. In a case where theestimation is not accurate enough, the system returns to the preparatoryphase. In a case where the estimation is accurate enough, the systemproceeds to the actual use phase.

(3) Actual Use Phase

In the actual use phase, the loss-on-ignition estimation apparatus 1estimates loss-on-ignition based on 10-minute calcination. The learnedmodel LM used by the loss-on-ignition estimation apparatus 1 in theactual use phase has been confirmed in the trial phase to be capable ofmaking accurate-enough estimation. In the actual use phase, it is notnecessary to carry out 60-minute calcination to determineloss-on-ignition. This eliminates the need for the operator to take timeto carry out 60-minute calcination to determine loss-on-ignition, andalso makes it possible to run the casting cycle C efficiently.

Note that the present embodiment employs a configuration in which thelearned model LM receives, as input, all of the following data: the sandweight data, the sand property data, the additive data, and thecalcination environment data. However, the present invention is notlimited to such. Out of these types of data, data which has a dominantinfluence on estimated loss-on-ignition is the sand weight data. Thesand property data, the additive data, and the calcination environmentdata are used to improve the accuracy of estimation of loss-on-ignition,and the learned model does not need to receive all of them as input.That is, any configuration can be employed, provided that (1) the sandweight data and (2) at least one of (i) the sand property data, (ii) theadditive data, and (iii) the calcination environment data are inputtedto the learned model LM. The phrase “at least one of (i) the sandproperty data, (ii) the additive data, and (iii) the calcinationenvironment data” herein means (a) a combination of these three types ofdata, (b) a combination of two types of data selected from the threetypes of data, or (c) one type of data selected from the three types ofdata.

The sand weight data does not necessarily need to contain sand weight atthe start of calcination and sand weight at the end of calcination,provided that the sand weight data contains a value of sand weightdetected at a specific point in time or values of sand weight detectedspecific points in time. For example, in a case where foundry sand usedas a sample is constant in weight, the sand weight data may contain onlysand weight at the end of calcination. The sand property data does notnecessarily need to contain all of the following: the sand-to-metalratio, the amount of new sand introduced, and the capacitance, providedthat the sand property data contains at least part of such information.The additive data does not necessarily need to contain all of thefollowing: the resin type, the amount of resin added, the hardeningagent type, and the amount of hardening agent added, provided that theadditive data contains at least part of such information. Thecalcination environment data does not necessarily need to contain all ofthe following: the furnace's internal temperature, the furnacetemperature, the ambient temperature, the gas pressure, the gasquantity, and the rate of temperature rise, provided that thecalcination environment data contains at least part of such information.

[Configuration of Loss-On-Ignition Estimation Apparatus]

A configuration of the loss-on-ignition estimation apparatus 1 isdiscussed with reference to FIG. 2. FIG. 2 is a block diagramillustrating the configuration of the loss-on-ignition estimationapparatus 1.

The loss-on-ignition estimation apparatus 1 is realized by a generalpurpose computer, and includes a processor 11, a primary memory 12, asecondary memory 13, an input-output interface 14, a communicationinterface 15, and a bus 16. The processor 11, the primary memory 12, thesecondary memory 13, the input-output interface 14, and thecommunication interface 15 are connected to one another through the bus16.

The secondary memory 13 has a loss-on-ignition estimation program P1 anda learned model LM stored therein. The processor 11 reads theloss-on-ignition estimation program P1 and the learned model LM from thesecondary memory 13 and load them into the primary memory 12. Then, theprocessor 11 carries out steps included in the loss-on-ignitionestimation method M1 in accordance with instructions contained in theloss-on-ignition estimation program P1 loaded in the primary memory 12.The learned model LM loaded in the primary memory 12 is used when theprocessor 11 carries out an estimation step M12 (described later) of theloss-on-ignition estimation method M1. Note that the phrase “thesecondary memory 13 has the loss-on-ignition estimation program P1stored therein” means that a source code or an executable file obtainedby compiling the source code is stored in the secondary memory 13. Thephrase “the secondary memory 13 has the learned model LM stored therein”means that parameters defining the learned model LM are stored in thesecondary memory 13.

A device that can be used as the processor 11 is, for example, a centralprocessing unit (CPU), a graphic processing unit (GPU), a digital signalprocessor (DSP), a micro processing unit (MPU), a floating point numberprocessing unit (FPU), a physics processing unit (PPU), amicrocontroller, or a combination of any of those listed above. Theprocessor 11 is also referred to as “arithmetic device”.

A device that can be used as the primary memory 12 is, for example, asemiconductor random access memory (RAM). The primary memory 12 is alsoreferred to as “main storage device”. A device that can be used as thesecondary memory 13 is, for example, a flash memory, a hard disk drive(HDD), a solid state drive (SSD), an optical disk drive (ODD), a floppydisk drive (FDD), or a combination of any of those listed above. Thesecondary memory 13 is also referred to as “auxiliary storage device”.Note that the secondary memory 13 may be contained in theloss-on-ignition estimation apparatus 1 or may be contained in anothercomputer (for example, a computer that constitutes a cloud server) thatis connected to the loss-on-ignition estimation apparatus 1 through theinput-output interface 14 or the communication interface 15. Note that,in the present embodiment, the memory of the loss-on-ignition estimationapparatus 1 is realized by two memories (the primary memory 12 and thesecondary memory 13); however, this does not imply any limitation. Thatis, the memory of the loss-on-ignition estimation apparatus 1 may berealized by a single memory. In this case, for example, a certainstorage area of the memory may be used as the primary memory 12 andanother storage area of the memory may be used as the secondary memory13.

The input-output interface 14 is configured to have input device(s)and/or output device(s) connected thereto. Examples of the input-outputinterface 14 include universal serial bus (USB), advanced technologyattachment (ATA), small computer system interface (SCSI), and peripheralcomponent interconnect (PCI) interfaces, and the like. The input deviceconnected to the input-output interface 14 can be, for example, the datalogger 5. Data acquired from the sensor group 4 in the loss-on-ignitionestimation method M1 is inputted to the loss-on-ignition estimationapparatus 1 via the data logger 5, and stored in the primary memory 12.The input device connected to the input-output interface 14 canalternatively be, for example, a keyboard, a mouse, a touchpad, amicrophone, or a combination of any of those listed above. Data acquiredfrom an operator in the loss-on-ignition estimation method M1 isinputted to the loss-on-ignition estimation apparatus 1 via such inputdevice(s) and stored in the primary memory 12. The output deviceconnected to the input-output interface 14 can be, for example, adisplay, a projector, a printer, a speaker, a headphone, or acombination of any of those listed above. Information to be presented tothe operator in the loss-on-ignition estimation method M1 is outputtedfrom the loss-on-ignition estimation apparatus 1 via such outputdevice(s). Note that the loss-on-ignition estimation apparatus 1 maycontain a keyboard serving as an input device and a display serving asan output device, like a laptop computer. Alternatively, theloss-on-ignition estimation apparatus 1 may contain a touch panel thatserves both as the input device and the output device, like a tabletcomputer.

The communication interface 15 is configured to have, connected thereto,another computer in a wired manner or wirelessly over a network.Examples of the communication interface 15 include Ethernet (registeredtrademark) and Wi-Fi (registered trademark) interfaces, and the like.Examples of the network that can be employed include personal areanetwork (PAN), local area network (LAN), campus area network (CAN),metropolitan area network (MAN), wide area network (WAN), global areanetwork (GAN), and an internetwork including any of such networks. Theinternetwork may be an intranet, an extranet, or the Internet. Data (forexample, learned model LM) that the loss-on-ignition estimationapparatus 1 acquires from another computer (for example,machine-learning apparatus 2) in the loss-on-ignition estimation methodM1, and data that the loss-on-ignition estimation apparatus 1 providesto another computer in the loss-on-ignition estimation method M1, aretransmitted and received over such network(s).

Note that, although the present embodiment employs a configuration inwhich a single processor (processor 11) is used to carry out theloss-on-ignition estimation method M1, the present invention is notlimited to such. That is, a configuration in which a plurality ofprocessors are used to carry out the loss-on-ignition estimation methodM1 may be employed. In this case, a plurality of processors which worktogether to carry out the loss-on-ignition estimation method M1 may beprovided in a single computer and configured to be communicable witheach other through a bus or may be provided in a respective plurality ofcomputers and configured to be communicable with each other over anetwork. For example, the following configuration can be employed: aprocessor contained in a computer constituting a cloud server and aprocessor contained in a computer owned by a user of the cloud serverwork together to carry out the loss-on-ignition estimation method M1.

Although the present embodiment employs a configuration in which thelearned model LM is stored in a memory (secondary memory 13) that iscontained in the computer in which a processor (processor 11) thatcarries out the loss-on-ignition estimation method M1 is contained, thepresent invention is not limited to such. That is, the followingconfiguration may be employed: the learned model LM is stored in amemory that is contained in a computer different from the computer inwhich the processor that carries out the loss-on-ignition estimationmethod M1 is contained. In this case, the computer in which the memoryhaving the learned model LM stored therein is contained is configured tobe communicable, over a network, with the computer in which theprocessor that carries out the loss-on-ignition estimation method M1 iscontained. For example, the following configuration can be employed: thelearned model LM is stored in a memory contained in a computerconstituting a cloud server; and a processor contained in a computerowned by a user of the cloud server carries out the loss-on-ignitionestimation method M1.

Although the present embodiment employs a configuration in which thelearned model LM is stored in a single memory (secondary memory 13), thepresent invention is not limited to such. That is, the followingconfiguration may be employed: the learned model LM is divided into aplurality of parts and stored in a respective plurality of memories. Inthis case, the plurality of memories in which the parts of the learnedmodel LM are stored may be provided in a single computer (which may beor may not be the computer in which a processor that carries out theloss-on-ignition estimation method M1 is contained) or in a respectiveplurality of different computers (which may or may not include thecomputer in which a processor that carries out the loss-on-ignitionestimation method M1 is contained). For example, the followingconfiguration may be employed: the learned model LM is divided into aplurality of parts and stored in a respective plurality of memoriescontained in a respective plurality of computers constituting a cloudserver.

[Flow of Loss-On-Ignition Estimation Method]

The following description will discuss a flow of the loss-on-ignitionestimation method M1 with reference to FIG. 3. FIG. 3 is a flowchartillustrating the loss-on-ignition estimation method M1.

The loss-on-ignition estimation method M1 includes a preprocessing stepM11 and an estimation step M12.

The preprocessing step M11 is a step in which the processor 11 preparesdata that is to be inputted to the learned model LM. In thepreprocessing step M11, the processor 11 reads, from the primary memory12, data provided from the data logger 5 and data inputted by anoperator, and carries out the following processing.

(1) The processor 11 determines time t0 at which the furnace 35 startedto operate, time t1 at which the furnace's internal temperature reacheda predetermined calcination temperature T1 (for example, 1000° C.), andtime t2 which is a predetermined calcination time Δt (for example, 10minutes) after the time t1.

(2) The processor 11 determines sand weight indicated by the electronicbalance 41 at the time t1 as being sand weight at the start ofcalcination, and determines sand weight indicated by the electronicbalance at the time t2 as being sand weight at the end of calcination.

(3) The processor 11 determines furnace's internal temperature T0indicated by the thermometer 42 at the time t0, and then carries outcalculation using (T1−T0)/(t1−t0) to find the rate of temperature rise.

(4) The processor 11 acquires data of furnace's internal temperature,furnace temperature, ambient temperature, gas pressure, and gas quantityindicated by the thermometer 42, the thermometer 43, the thermometer 44,the pressure gauge 45, and the flowmeter 46, respectively, in a periodfrom the time t1 to the time t2 (period during which calcination step iscarried out). The processor 11 then calculates, from such data, theaverage internal temperature, average furnace temperature, averageambient temperature, average gas pressure, and average gas quantity.

(5) The processor 11 writes, into the primary memory 12, the sand weightat the start of calcination, the sand weight at the end of calcination,the rate of temperature rise, the average furnace's internaltemperature, the average furnace temperature, the average ambienttemperature, the average gas pressure, and the average gas quantitywhich have been determined or calculated through the foregoingprocessing.

The estimation step M12 is a step in which the processor 11 estimatesloss-on-ignition with use of the learned model LM. In the estimationstep M12, the processor 11 reads sand weight data, sand property data,additive data, and calcination environment data from the primary memory12, and inputs them to the learned model LM. The processor 11 thenwrites, into the primary memory 12, the estimated loss-on-ignitionoutputted from the learned model LM.

As described earlier, the sand weight data contains sand weight at thestart of calcination and sand weight at the end of calcination. The sandproperty data contains sand weight at the start of calcination, sandweight at the end of calcination, sand type, sand-to-metal ratio, amountof new sand introduced, and capacitance. The additive data containsresin type, amount of resin added, hardening agent type, and amount ofhardening agent added. The calcination environment data containsfurnace's internal temperature, furnace temperature, ambienttemperature, gas pressure, gas quantity, and rate of temperature rise.Furthermore, as described earlier, the learned model LM generates, asoutput, an estimated value of loss-on-ignition defined by (W0−W60)/W0,where W0 is the weight of dried but not calcined foundry sand and W60 isthe weight of calcined foundry sand, in which the calcination is carriedout at 1000° C. for 60 minutes.

Note that the learned model LM may be configured to output estimatedsand weight after 60 minutes (which is an example of “predeterminedperiod” in the claims) from the start of calcination (this sand weightis the above-mentioned “W60”), instead of outputting the estimatedloss-on-ignition. In this case, in the estimation step M12, theprocessor 11 calculates estimated loss-on-ignition by substituting thesand weight W0 at the start of calcination and the sand weight W60outputted from the learned model LM into the expression (W0−W60)/W0.

The loss-on-ignition estimation method M1 may further include an outputstep involving outputting the loss-on-ignition estimated in theestimation step M12. In the output step, the processor 11 outputs theestimated loss-on-ignition to a display to present the loss-on-ignitionto an operator that controls the casting cycle C. This allows theoperator to optimize a sand conditioning cycle in accordance with theloss-on-ignition of recovered sand or reclaimed sand. Alternatively, theprocessor 11 provides the estimated loss-on-ignition to a linecontroller that controls the casting cycle C. This allows the linecontroller to optimize the casting cycle C in accordance with theloss-on-ignition of recovered sand or reclaimed sand.

The loss-on-ignition estimation method M1 may further include acondition setting step (corresponding to condition setting step C32described later) that involves setting, in accordance with theloss-on-ignition estimated in the estimation step M12, condition(s) inwhich step(s) of the casting cycle C is/are carried out. In thecondition setting step, for example, the processor 11 sets conditions inwhich a sand reclamation step C24 (described later) is carried out sothat the conditions are appropriate for the loss-on-ignition estimatedin the estimation step M12. Alternatively, the processor 11 setsconditions in which a new sand introducing step C26 (described later) iscarried out so that the conditions are appropriate for theloss-on-ignition estimated in the estimation step M12. Alternatively,the processor 11 sets conditions in which a mixing step C11 (describedlater) is carried out so that the conditions are appropriate for theloss-on-ignition estimated in the estimation step M12. As such, theoptimization of the casting cycle C, in accordance with theloss-on-ignition of recovered sand or reclaimed sand, can also beachieved by causing the processor 11 to carry out the condition settingstep.

[Configuration of Machine-Learning Apparatus]

The following description will discuss a configuration of themachine-learning apparatus 2 with reference to FIG. 4. FIG. 4 is a blockdiagram illustrating the configuration of the machine-learning apparatus2.

The machine-learning apparatus 2 is realized by a general purposecomputer, and includes a processor 21, a primary memory 22, a secondarymemory 23, an input-output interface 24, a communication interface 25,and a bus 26. The processor 21, the primary memory 22, the secondarymemory 23, the input-output interface 24, and the communicationinterface 25 are connected to one another through the bus 26.

The secondary memory 23 has a machine-learning program P2 and adataset-for-learning DS stored therein. The dataset-for-learning DS is aset of training data DS1, training data DS2, . . . and so on. Theprocessor 21 reads the machine-learning program P2 from the secondarymemory 23 and loads it into the primary memory 22. Then, the processor21 carries out steps included in the machine-learning method M2 inaccordance with instructions contained in the machine-learning programP2 loaded in the primary memory 22. The dataset-for-learning DS storedin the secondary memory 23 is constructed in a step M21 of constructingdataset for learning (described later) of the machine-learning methodM2, and used in a step M22 of constructing learned model (describedlater) of the machine-learning method M2. The learned model LMconstructed in the step M22 of constructing learned model of themachine-learning method M2 is also stored in the secondary memory 23.Note that the phrase “the secondary memory 23 has the machine-learningprogram P2 stored therein” means that a source code or an executablefile obtained by compiling the source code is stored in the secondarymemory 23. The phrase “the secondary memory 23 has the learned model LMstored therein” means that parameters defining the learned model LM arestored in the secondary memory 23.

A device that can be used as the processor 21 is, for example, a centralprocessing unit (CPU), a graphic processing unit (GPU), a digital signalprocessor (DSP), a micro processing unit (MPU), a floating point numberprocessing unit (FPU), a physics processing unit (PPU), amicrocontroller, or a combination of any of those listed above. Theprocessor 21 is also referred to as “arithmetic device”.

A device that can be used as the primary memory 22 is, for example, asemiconductor random access memory (RAM). The primary memory 22 is alsoreferred to as “main storage device”. A device that can be used as thesecondary memory 23 is, for example, a flash memory, a hard disk drive(HDD), a solid state drive (SSD), an optical disk drive (ODD), a floppydisk drive (FDD), or a combination of any of those listed above. Thesecondary memory 23 is also referred to as “auxiliary storage device”.Note that the secondary memory 23 may be contained in themachine-learning apparatus 2 or may be contained in another computer(for example, a computer that constitutes a cloud server) that isconnected to the machine-learning apparatus 2 through the input-outputinterface 24 or the communication interface 25. Note that, in thepresent embodiment, the memory of the machine-learning apparatus 2 isrealized by two memories (the primary memory 22 and the secondary memory23); however, this does not imply any limitation. That is, the memory ofthe machine-learning apparatus 2 may be realized by a single memory. Inthis case, for example, a certain storage area of the memory may be usedas the primary memory 22 and another storage area of the memory may beused as the secondary memory 23.

The input-output interface 24 is configured to have input device(s)and/or output device(s) connected thereto. Examples of the input-outputinterface 24 include universal serial bus (USB), advanced technologyattachment (ATA), small computer system interface (SCSI), and peripheralcomponent interconnect (PCI) interfaces, and the like. The input deviceconnected to the input-output interface 24 can be, for example, the datalogger 5. Data acquired from the sensor group 4 in the machine-learningmethod M2 is inputted to the machine-learning apparatus 2 via the datalogger 5, and stored in the primary memory 22. The input deviceconnected to the input-output interface 24 can alternatively be, forexample, a keyboard, a mouse, a touchpad, a microphone, or a combinationof any of those listed above. Data acquired from an operator in themachine-learning method M2 is inputted to the machine-learning apparatus2 via such input device(s) and stored in the primary memory 22. Theoutput device connected to the input-output interface 24 can be, forexample, a display, a projector, a printer, a speaker, a headphone, or acombination of any of those listed above. Information to be presented tothe operator in the machine-learning method M2 is outputted from themachine-learning apparatus 2 via such output device(s). Note that themachine-learning apparatus 2 may contain a keyboard serving as an inputdevice and a display serving as an output device, like a laptopcomputer. Alternatively, the machine-learning apparatus 2 may contain atouch panel that serves both as the input device and the output device,like a tablet computer.

The communication interface 25 is configured to have, connected thereto,another computer in a wire manner or wirelessly over a network. Examplesof the communication interface 25 include Ethernet (registeredtrademark) and Wi-Fi (registered trademark) interfaces, and the like.Examples of the network that can be employed include personal areanetwork (PAN), local area network (LAN), campus area network (CAN),metropolitan area network (MAN), wide area network (WAN), global areanetwork (GAN), and an internetwork including any of such networks. Theinternetwork may be an intranet, an extranet, or the Internet. Data (forexample, learned model LM) that the machine-learning apparatus 2presents to another computer (for example, loss-on-ignition estimationapparatus 1) is transmitted and received over such network(s).

Note that, although the present embodiment employs a configuration inwhich a single processor (processor 21) is used to carry out themachine-learning method M2, the present invention is not limited tosuch. That is, a configuration in which a plurality of processors areused to carry out the machine-learning method M2 may be employed. Inthis case, a plurality of processors which work together to carry outthe machine-learning method M2 may be provided in a single computer andconfigured to be communicable with each other through a bus or may beprovided in a respective plurality of computers and configured to becommunicable with each other over a network. For example, the followingconfiguration can be employed: a processor contained in a computerconstituting a cloud server and a processor contained in a computerowned by a user of the cloud server work together to carry out themachine-learning method M2.

Although the present embodiment employs a configuration in which thedataset-for-learning DS is stored in a memory (secondary memory 23) thatis contained in the computer in which a processor (processor 21) thatcarries out the machine-learning method M2 is contained, the presentinvention is not limited to such. That is, the following configurationmay be employed: the dataset-for-learning DS is stored in a memory thatis contained in a computer different from the computer in which theprocessor that carries out the machine-learning method M2 is contained.In this case, the computer in which the memory having thedataset-for-learning DS stored therein is contained is configured to becommunicable, over a network, with the computer in which the processorthat carries out the machine-learning method M2 is contained. Forexample, the following configuration can be employed: thedataset-for-learning DS is stored in a memory contained in a computerconstituting a cloud server; and a processor contained in a computerowned by a user of the cloud server carries out the machine-learningmethod M2.

Although the present embodiment employs a configuration in which thedataset-for-learning DS is stored in a single memory (secondary memory23), the present invention is not limited to such. That is, thefollowing configuration may be employed: the dataset-for-learning DS isdivided into a plurality of parts and stored in a respective pluralityof memories. In this case, the plurality of memories in which the partsof the dataset-for-learning DS are stored may be provided in a singlecomputer (which may be or may not be the computer in which a processorthat carries out the machine-learning method M2 is contained) or in arespective plurality of different computers (which may or may notinclude the computer in which a processor that carries out themachine-learning method M2 is contained). For example, the followingconfiguration may be employed: the dataset-for-learning DS is dividedinto a plurality of parts and stored in a respective plurality ofmemories contained in a respective plurality of computers constituting acloud server.

Although the present embodiment employs a configuration in whichdifferent processors (processor 11 and processor 21) are used to carryout the loss-on-ignition estimation method M1 and the machine-learningmethod M2, the present invention is not limited to such. That is, asingle processor may be used to carry out the loss-on-ignitionestimation method M1 and the machine-learning method M2. In this case,the processor carries out the machine-learning method M2, and therebythe learned model LM is stored in a memory that is contained in the samecomputer as the processor. The processor will use the learned model LMstored in this memory when carrying out the loss-on-ignition estimationmethod M1.

[Flow of Machine-Learning Method]

The following description will discuss a flow of the machine-learningmethod M2 with reference to FIG. 5. FIG. 5 is a flowchart illustratingthe machine-learning method M2.

The machine-learning method M2 includes a step M21 of constructingdataset for learning and a step M22 of constructing learned model.

The step M21 of constructing dataset for leaning is a step in which theprocessor 21 constructs a dataset-for-learning DS which is a set oftraining data DS1, training data DS2, . . . and so on.

Each training data DSi (i=1, 2, . . . and so on) contains sand weightdata, sand property data, additive data, and calcination environmentdata. The sand weight data, sand property data, additive data, andcalcination environment data contained in the training data DSi are thesame in type as sand weight data, sand property data, additive data, andcalcination environment data which are to be inputted to the learnedmodel LM. In the step M21 of constructing dataset for learning, theprocessor 21 acquires such data in the same manner as theloss-on-ignition estimation apparatus 1. The training data DSi alsocontains, as a label, loss-on-ignition defined by (W0−W60)/W0, where W0is the weight of dried but not calcined foundry sand and W60 is theweight of calcined foundry sand, in which the calcination is carried outat 1000° C. for 60 minutes. In the step M21 of constructing dataset forlearning, the processor 21 calculates the loss-on-ignition (1) using, asW0, sand weight detected at the start of calcination, (2) using, as W60,sand weight detected at the point in time after 60 minutes from thestart of calcination, and (3) using the expression “(W0−W60)/W0”. Theprocessor 21 then causes the secondary memory 23 to store the acquiredsand weight data, sand property data, additive data, and calcinationenvironment data and the calculated loss-on-ignition such that theacquired sand weight data, sand property data, additive data, andcalcination environment data are associated with the calculatedloss-on-ignition. The processor 21 repeats the above-described processto construct the dataset-for-learning DS.

The step M22 of constructing learned model is a step in which theprocessor 21 constructs the learned model LM. In the step M22 ofconstructing learned model, the processor 21 constructs the learnedmodel LM by means of supervised learning using the dataset-for-learningDS. The processor 21 then causes the secondary memory 23 to store theconstructed learned model LM.

[Flow of Casting Cycle]

The following description will discuss a flow of a casting cycle C towhich the loss-on-ignition estimation system S is applied, withreference to FIG. 6. FIG. 6 is a flowchart illustrating the castingcycle C.

As shown in FIG. 6, the casting cycle C includes a casting phase C1 anda sand reclamation phase C2.

The casting phase C1 involves carrying out casting using foundry sandreclaimed through the sand reclamation phase C2 (such foundry sand is amixture of reclaimed sand and new sand, described later). The castingphase C1 can be comprised of, for example, as shown in FIG. 6, a mixingstep C11, a molding step C12, a mold drawing step C13, a mold washingstep C14, a mold closing step C15, a pouring step C16, a cooling stepC17, and a flask releasing step C18.

The mixing step C11 involves adding additives including a resin and ahardening agent to the foundry sand reclaimed through the sandreclamation phase C2 and carrying out mixing. The molding step C12involves making a mold by filling a flask with the foundry sand obtainedthrough mixing in the mixing step C11. In the molding step C12, an uppermold that corresponds to an upper part of the mold and a lower mold thatcorresponds to a lower part of the mold are made. The mold drawing stepC13 involves removing the upper and lower molds made in the molding stepC12 from the flask. The mold washing step C14 involves applying a moldwash to the surfaces, facing the product, of the upper and lower moldsremoved in the mold drawing step C13. The mold closing step C15 involvesobtaining a mold by combining the upper and lower molds which had themold wash applied in the mold washing step C14. The pouring step C16involves pouring a molten metal into the mold obtained in the moldclosing step C15. The cooling step C17 involves cooling the molten metalpoured into the mold in the pouring step C16. The cooled molten metalsolidifies within the mold to become a casting. The flask releasing stepC18 involves: breaking down the mold into sand masses by applyingvibration to the mold; and removing the casting obtained in the coolingstep C17.

The sand reclamation phase C2 involves reclaiming foundry sand from thesand masses obtained in the casting phase C1. The sand reclamation phaseC2 can be comprised of, for example, as shown in FIG. 6, a crushing stepC21, a separating step C22, a pre-reclamation cooling step C23, a sandreclamation step C24, a post-reclamation cooling step C25, and a newsand introducing step C26.

The crushing step C21 involves crushing the sand masses obtained in thecasting phase C1 into sand grains by applying vibration to the sandmasses. The sand grains obtained in the crushing step C21 contain notonly the grains of foundry sand to be reclaimed but also particles otherthan the foundry sand such as iron pieces and debris. The surfaces ofthe grains of foundry sand to be reclaimed have combustible residuessuch as a resin attached thereon. The separating step C22 involvesseparating particles other than the foundry sand from the sand grainsobtained in the crushing step C21. The foundry sand obtained in theseparating step C22, that is, foundry sand from which the combustibleresidues attached on the surfaces of the sand grains have not yet beenremoved, may be hereinafter referred to as “recovered sand”. Thepre-reclamation cooling step C23 involves cooling the recovered sandobtained in the separating step C22. The sand reclamation step C24involves removing the combustible residues from the grains of therecovered sand cooled in the pre-reclamation cooling step C23. Thefoundry sand obtained in the sand reclamation step C24, that is, foundrysand from which the combustible residues on the surfaces have beenremoved, may be hereinafter referred to as “reclaimed sand”. Thepost-reclamation cooling step C25 involves cooling the reclaimed sandobtained in the sand reclamation step C24. The new sand introducing stepC26 involves adding new sand, that is, unused foundry sand, to thereclaimed sand obtained in the sand reclamation step C24. A mixture ofthe reclaimed sand and the new sand obtained in the new sand introducingstep C26 is used as foundry sand in the subsequent casting phase C1.

In order to maintain the quality of foundry sand for use in the castingphase C1, it is necessary to appropriately set the condition(s) in whichthe sand reclamation step C24 is carried out (hereinafter may bereferred to as “reclamation condition(s)”), the condition(s) in whichthe new sand introducing step C26 is carried out (hereinafter may bereferred to as “new sand introduction condition(s)”), and thecondition(s) in which the mixing step C11 is carried out (hereinaftermay be referred to as “mixing condition(s)”). The new sand introductioncondition(s) can include, for example, the amount of new sand introducedin the new sand introducing step C26. Note that the reclamationcondition(s) and the mixing condition(s) will be described later withreference to a different drawing.

In view of the above, in the casting cycle C, the above-statedconditions are set based on the loss-on-ignition of recovered sandand/or reclaimed sand. To achieve this, the casting cycle C includes: aloss-on-ignition estimation step C31 involving estimating theloss-on-ignition of recovered sand and/or reclaimed sand; and acondition setting step C32 involving setting at least one of (i) thereclamation conditions, (ii) the new sand introduction conditions, and(iii) the mixing conditions on the basis of the loss-on-ignitionestimated in the loss-on-ignition estimation step C31. Theloss-on-ignition estimation step C31 is carried out by theloss-on-ignition estimation apparatus 1, as described earlier. Thecondition setting step C32 may be carried out by the loss-on-ignitionestimation apparatus 1, by an operator who has obtained theloss-on-ignition from the loss-on-ignition estimation apparatus 1, or bya line controller which has acquired the loss-on-ignition from theloss-on-ignition estimation apparatus 1, as described earlier. Specificexamples of the condition setting step C32 are as follows.

A first specific example is feedforward condition setting by which thereclamation condition(s) is/are set in accordance with theloss-on-ignition of recovered sand. A second specific example isfeedback condition setting by which the reclamation condition(s) is/areset in accordance with the loss-on-ignition of reclaimed sand. A thirdspecific example is a combination of feedback condition setting andfeedforward condition setting by which the reclamation condition(s)is/are set in accordance with both the loss-on-ignition of recoveredsand and the loss-on-ignition of reclaimed sand. The third specificexample includes, for example, an aspect in which the reclamationcondition(s) is/are set in accordance with the weighted average of theloss-on-ignition of recovered sand and the loss-on-ignition of reclaimedsand. A fourth specific example is feedforward condition setting bywhich the mixing condition(s) is/are set in accordance with theloss-on-ignition of reclaimed sand. Note that the setting of the mixingcondition(s) may be carried out when the strength of the mold made inthe molding step C12 is outside a predetermined control range. A fifthspecific example is condition setting by which the new sand introductioncondition(s) is/are set in accordance with the loss-on-ignition ofreclaimed sand.

Note that, in the loss-on-ignition estimation step C31, (1) only theloss-on-ignition of recovered sand may be estimated, (2) only theloss-on-ignition of reclaimed sand may be estimated, or (3) both theloss-on-ignition of recovered sand and the loss-on-ignition of reclaimedsand may be estimated. In the case (1), the foregoing condition settingstep C32 in accordance with the first specific example can be employed.In the case (2), a portion of or all of the foregoing condition settingsteps C32 in accordance with the second, fourth, and fifth specificexample can be employed. In the case (3), a portion of or all of theforegoing condition setting steps C32 in accordance with the third,fourth, and fifth specific example can be employed.

[Specific Examples of Reclamation Conditions and Setting of ReclamationConditions]

In a case where the sand reclamation step C24 is carried out with use ofa sand reclaimer, the reclamation conditions can be rephrased asconditions in which the sand reclaimer operates. The sand reclaimer canbe, for example, a sand reclaimer 7 illustrated in FIG. 7.

As illustrated in FIG. 7, the sand reclaimer 7 includes a reclaimingsection 70. The reclaiming section 70 includes a rotary drum 71 and atleast one pressure roller 72 which is pressed against the inner surfaceof the side wall of the rotary drum 71. When recovered sand isintroduced and the rotary drum 71 is rotated, grains of the recoveredsand flying due to centrifugal force are ground and polished between theside wall of the rotary drum 71 and the pressure roller 72, andcombustible residues are removed. The rotary drum 71 has, at the topthereof, a flange 73 called an orifice projecting inward. It is possibleto keep the grains of the introduced recovered sand within the rotarydrum 71 for a long time by configuring the flange 73 to project greatly.

In a case where the sand reclamation step C24 is carried out with use ofthe sand reclaimer 7, the reclamation conditions include the amount ofrecovered sand introduced, the number of rotations, the pressure appliedby the roller, and the amount of projection of the flange. In a casewhere the sand reclaimer 7 carries out sand reclamation batchwise, thetime for which sand reclamation is carried out is also included in thereclamation conditions. The definition for the respective reclamationconditions and an overview of the condition setting step C32 for eachreclamation condition are shown in the following table. Note that thehighest-priority reclamation condition is the pressure applied by theroller. In a case where changing only the pressure applied by the rolleris sufficient to optimize the operation of the sand reclaimer 7, settingof the other reclamation conditions may be omitted. The lowest-priorityreclamation condition is the amount of recovered sand introduced.Setting of the amount of recovered sand introduced may be carried outonly in a case where changes of other reclamation conditions are notsufficient to optimize the operation of the sand reclaimer 7.

TABLE 1 Reclamation conditions Definition Overview of condition settingstep Amount of The amount of recovered sand Reduce the amount ofrecovered recovered sand introduced per unit time (in a sand introducedas introduced case of continuous process) or loss-on-ignition ofrecovered sand per batch (in a case of batch and/or loss-on-ignition ofprocess) reclaimed sand increases Number of The number of rotations ofIncrease the number of rotations rotations rotary drum 71 per unit timeas loss-on-ignition of recovered sand and/or loss-on-ignition ofreclaimed sand increases Pressure applied The force applied by pressureIncrease pressure applied by by roller roller 72 to inner surface ofroller as loss-on-ignition of rotary drum 71 recovered sand and/orloss-on-ignition of reclaimed sand increases Amount of Amount ofprojection of flange Increase the amount of projection projection offlange 73 of flange as loss-on-ignition of recovered sand and/orloss-on-ignition of reclaimed sand increases Time for which Time forwhich rotary drum Increase the time for which sand sand reclamation 71is rotated reclamation is carried out as is carried out (time for whichsand loss-on-ignition of recovered sand (batch process) reclamation iscarried out) and/or loss-on-ignition of reclaimed sand increases

Note that, in the sand reclamation step C24, removal of combustibleresidues by calcination may be carried out in addition to or instead ofthe foregoing removal of combustible residues by polishing. In thiscase, the calcination temperature and the calcination time are alsoexamples of reclamation conditions. The definitions for the respectivereclamation conditions and an overview of the condition setting step C32for each reclamation condition are shown in the following table. Notethat whether or not to carry out the setting of the calcinationtemperature is determined preferably in accordance with the type ofresin added to foundry sand. For example, in a case where the resinadded to the foundry sand is water glass, it is preferable that thesetting of the calcination temperature be omitted.

TABLE 2 Reclamation Overview of condition conditions Definition settingstep Calcination Furnace temperature or Raise calcination temperaturefurnace's internal temperature as temperature when loss-on-ignition ofrecovered sand is recovered sand and/or calcined loss-on-ignition ofreclaimed sand increases Calcination Time for which recovered Increasecalcination time time sand is calcined at a as loss-on-ignition ofpredetermined calcination recovered sand and/or temperatureloss-on-ignition of (e.g., 1000° C.) reclaimed sand increases

[Specific Examples of Mixing Conditions and Setting of Mixing Condition]

In a case where the mixing step C11 is carried out with use of a mixingmachine, mixing conditions can be rephrased as conditions in which themixing machine operates. The mixing machine can be, for example, amixing machine 8 illustrated in FIG. 8.

As illustrated in FIG. 8, the mixing machine 8 includes a mixing section80 which includes mixing blades 81. By introducing foundry sand andadditives (resin and hardening agent) into the mixing section 80 androtating the mixing blade 81, self-hardening foundry sand can begenerated. In a case where the mixing step C11 is carried out with useof the mixing machine 8, the mixing conditions include the amounts ofadditives (the amount of resin added and the amount of hardening agentadded), the number of rotations of mixing blades, and the amount ofmixed sand to be conditioned. The definitions for the respective mixingconditions and an overview of the condition setting step C32 for eachmixing condition are shown in the following table. Note that a change inthe amount of mixed sand to be conditioned results in an increase ordecrease in amount of resulting reclaimed sand. Therefore, the settingof the amount of mixed sand to be conditioned is preferably omitted in acase where changes of other mixing conditions are sufficient to optimizethe operation of the mixing machine 8.

TABLE 3 Mixing Overview of condition conditions Definition setting stepAmounts of The amounts of additives Increase the amounts of additivesadded per unit time (in a additives as loss-on-ignition case ofcontinuous process) of recovered sand and/or or per batch (in a case ofloss-on-ignition of batch process) reclaimed sand increases Number ofThe number of rotations of Increase the number of rotations of mixingblades per unit time rotations of mixing blades mixing asloss-on-ignition of blades recovered sand and/or loss-on-ignition ofreclaimed sand increases Amount of The amount of mixed sand Reduce theamount of mixed sand to be conditioned per unit mixed sand to be to betime (in a case of conditioned as conditioned continuous process) or perloss-on-ignition of batch (in a case of batch recovered sand and/orprocess) loss-on-ignition of reclaimed sand increases

The present invention is not limited to the embodiments, but can bealtered by a skilled person in the art within the scope of the claims.The present invention also encompasses, in its technical scope, anyembodiment derived by combining technical means disclosed in differingembodiments.

REFERENCE SIGNS LIST

-   -   1 loss-on-ignition estimation apparatus    -   11 processor    -   12 primary memory    -   13 secondary memory    -   14 input-output interface    -   15 communication interface    -   16 bus    -   M1 loss-on-ignition estimation method    -   M11 preprocessing step    -   M12 estimation step    -   2 machine-learning apparatus    -   21 processor    -   22 primary memory    -   23 secondary memory    -   24 input-output interface    -   25 communication interface    -   26 bus    -   M2 machine-learning method    -   M21 step of constructing dataset for learning    -   M22 step of constructing learned model    -   S loss-on-ignition estimation system

1. An apparatus configured to estimate loss-on-ignition, comprising atleast one processor configured to carry out an estimation step, theestimation step comprising estimating a loss-on-ignition of foundry sandwith use of a learned model constructed by means of machine learning,wherein: the learned model is configured to receive, as input, (1) sandweight data relating to a weight of the foundry sand detected in acalcination period and (2) at least one of (i) sand property datarelating to one or more properties of the foundry sand, (ii) additivedata relating to one or more additives added to the foundry sand, and(iii) calcination environment data relating to a calcination environmentdetected in the calcination period; and the learned model is configuredto generate, as output, an estimated loss-on-ignition of the foundrysand or an estimated weight of the foundry sand after a predeterminedperiod of calcination, the predetermined period being longer than thecalcination period.
 2. The apparatus according to claim 1, wherein: thesand weight data contains (i) a weight of the foundry sand detected at apoint in time at which the calcination period started and (ii) a weightof the foundry sand detected at a point in time at which the calcinationperiod ended; and the calcination period is shorter than 60 minutes. 3.The apparatus according to claim 1, wherein the sand property datacontains at least one of (i) sand type, (ii) sand-to-metal ratio, (iii)amount of new sand introduced, and (iv) capacitance.
 4. The apparatusaccording to claim 1, wherein the additive data contains at least one of(i) resin type, (ii) amount of resin added, (iii) hardening agent type,and (iv) amount of hardening agent added.
 5. The apparatus according toclaim 1, wherein the calcination environment data contains at least oneof (i) furnace's internal temperature, (ii) furnace temperature, (iii)ambient temperature, (iv) gas pressure, and (v) gas quantity.
 6. Theapparatus according to claim 1, wherein the at least one processorfurther carries out a condition setting step, the condition setting stepcomprising setting, in accordance with the loss-on-ignition estimated inthe estimation step, one or more conditions in which one or more stepsincluded in a cycle of conditioning of the foundry sand are carried out.7. The apparatus according to claim 6, wherein the one or moreconditions are one or more of (i) conditions in which a sand reclamationstep is carried out, (ii) conditions in which a mixing step is carriedout, and (iii) conditions in which a new sand introducing step iscarried out.
 8. A method of loss-on-ignition estimation, comprising anestimation step in which at least one processor estimates aloss-on-ignition of foundry sand with use of a learned model constructedby means of machine learning, wherein: the learned model is configuredto receive, as input, (1) sand weight data relating to a weight of thefoundry sand detected in a calcination period and (2) at least one of(i) sand property data relating to one or more properties of the foundrysand, (ii) additive data relating to one or more additives added to thefoundry sand, and (iii) calcination environment data relating to acalcination environment detected in the calcination period; and thelearned model is configured to generate, as output, an estimatedloss-on-ignition of the foundry sand or an estimated weight of thefoundry sand after a predetermined period of calcination, thepredetermined period being longer than the calcination period.
 9. Amachine-learning apparatus comprising at least one processor configuredto carry out a construction step, the construction step comprisingconstructing, by means of supervised learning using adataset-for-learning, a learned model configured to estimate aloss-on-ignition of foundry sand, wherein: the learned model isconfigured to receive, as input, (1) sand weight data relating to aweight of the foundry sand detected in a calcination period and (2) atleast one of (i) sand property data relating to one or more propertiesof the foundry sand, (ii) additive data relating to one or moreadditives added to the foundry sand, and (iii) calcination environmentdata relating to a calcination environment detected in the calcinationperiod; and the learned model is configured to generate, as output, anestimated loss-on-ignition of the foundry sand or an estimated weight ofthe foundry sand after a predetermined period of calcination, thepredetermined period being longer than the calcination period. 10.(canceled)